Data Visualization
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.0 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.1 ✔ tibble 3.2.0
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library (esquisse)library (lubridate)library (RColorBrewer)
Let’s load our dataset.
= read_csv ("dc_bike.csv" )
Rows: 17379 Columns: 15
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): dteday, mnth, holiday, weekday, workingday, weathersit
dbl (9): instant, hr, temp, atemp, hum, windspeed, casual, registered, count
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
spc_tbl_ [17,379 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ instant : num [1:17379] 1 2 3 4 5 6 7 8 9 10 ...
$ dteday : chr [1:17379] "1/1/2011" "1/1/2011" "1/1/2011" "1/1/2011" ...
$ mnth : chr [1:17379] "Jan" "Jan" "Jan" "Jan" ...
$ hr : num [1:17379] 0 1 2 3 4 5 6 7 8 9 ...
$ holiday : chr [1:17379] "NotHoliday" "NotHoliday" "NotHoliday" "NotHoliday" ...
$ weekday : chr [1:17379] "Saturday" "Saturday" "Saturday" "Saturday" ...
$ workingday: chr [1:17379] "NotWorkingDay" "NotWorkingDay" "NotWorkingDay" "NotWorkingDay" ...
$ weathersit: chr [1:17379] "NoPrecip" "NoPrecip" "NoPrecip" "NoPrecip" ...
$ temp : num [1:17379] 0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ...
$ atemp : num [1:17379] 0.288 0.273 0.273 0.288 0.288 ...
$ hum : num [1:17379] 0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ...
$ windspeed : num [1:17379] 0 0 0 0 0 0.0896 0 0 0 0 ...
$ casual : num [1:17379] 3 8 5 3 0 0 2 1 1 8 ...
$ registered: num [1:17379] 13 32 27 10 1 1 0 2 7 6 ...
$ count : num [1:17379] 16 40 32 13 1 1 2 3 8 14 ...
- attr(*, "spec")=
.. cols(
.. instant = col_double(),
.. dteday = col_character(),
.. mnth = col_character(),
.. hr = col_double(),
.. holiday = col_character(),
.. weekday = col_character(),
.. workingday = col_character(),
.. weathersit = col_character(),
.. temp = col_double(),
.. atemp = col_double(),
.. hum = col_double(),
.. windspeed = col_double(),
.. casual = col_double(),
.. registered = col_double(),
.. count = col_double()
.. )
- attr(*, "problems")=<externalptr>
instant dteday mnth hr
Min. : 1 Length:17379 Length:17379 Min. : 0.00
1st Qu.: 4346 Class :character Class :character 1st Qu.: 6.00
Median : 8690 Mode :character Mode :character Median :12.00
Mean : 8690 Mean :11.55
3rd Qu.:13034 3rd Qu.:18.00
Max. :17379 Max. :23.00
holiday weekday workingday weathersit
Length:17379 Length:17379 Length:17379 Length:17379
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
temp atemp hum windspeed
Min. :0.020 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.340 1st Qu.:0.3333 1st Qu.:0.4800 1st Qu.:0.1045
Median :0.500 Median :0.4848 Median :0.6300 Median :0.1940
Mean :0.497 Mean :0.4758 Mean :0.6272 Mean :0.1901
3rd Qu.:0.660 3rd Qu.:0.6212 3rd Qu.:0.7800 3rd Qu.:0.2537
Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :0.8507
casual registered count
Min. : 0.00 Min. : 0.0 Min. : 1.0
1st Qu.: 4.00 1st Qu.: 34.0 1st Qu.: 40.0
Median : 17.00 Median :115.0 Median :142.0
Mean : 35.68 Mean :153.8 Mean :189.5
3rd Qu.: 48.00 3rd Qu.:220.0 3rd Qu.:281.0
Max. :367.00 Max. :886.0 Max. :977.0
Let’s do a bit of variable type conversion before we proceed.
= bike %>% mutate_if (is.character, as_factor)
Examine structure
tibble [17,379 × 15] (S3: tbl_df/tbl/data.frame)
$ instant : num [1:17379] 1 2 3 4 5 6 7 8 9 10 ...
$ dteday : Factor w/ 731 levels "1/1/2011","1/2/2011",..: 1 1 1 1 1 1 1 1 1 1 ...
$ mnth : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 1 1 1 1 1 1 1 1 1 ...
$ hr : num [1:17379] 0 1 2 3 4 5 6 7 8 9 ...
$ holiday : Factor w/ 2 levels "NotHoliday","Holiday": 1 1 1 1 1 1 1 1 1 1 ...
$ weekday : Factor w/ 7 levels "Saturday","Sunday",..: 1 1 1 1 1 1 1 1 1 1 ...
$ workingday: Factor w/ 2 levels "NotWorkingDay",..: 1 1 1 1 1 1 1 1 1 1 ...
$ weathersit: Factor w/ 4 levels "NoPrecip","Misty",..: 1 1 1 1 1 2 1 1 1 1 ...
$ temp : num [1:17379] 0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ...
$ atemp : num [1:17379] 0.288 0.273 0.273 0.288 0.288 ...
$ hum : num [1:17379] 0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ...
$ windspeed : num [1:17379] 0 0 0 0 0 0.0896 0 0 0 0 ...
$ casual : num [1:17379] 3 8 5 3 0 0 2 1 1 8 ...
$ registered: num [1:17379] 13 32 27 10 1 1 0 2 7 6 ...
$ count : num [1:17379] 16 40 32 13 1 1 2 3 8 14 ...
Also convert the hour variable to a factor and the date variable to a date object.
= bike %>% mutate (hr = as_factor (hr)) %>% mutate (dteday = mdy (dteday))
Double-check structure
tibble [17,379 × 15] (S3: tbl_df/tbl/data.frame)
$ instant : num [1:17379] 1 2 3 4 5 6 7 8 9 10 ...
$ dteday : Date[1:17379], format: "2011-01-01" "2011-01-01" ...
$ mnth : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 1 1 1 1 1 1 1 1 1 ...
$ hr : Factor w/ 24 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
$ holiday : Factor w/ 2 levels "NotHoliday","Holiday": 1 1 1 1 1 1 1 1 1 1 ...
$ weekday : Factor w/ 7 levels "Saturday","Sunday",..: 1 1 1 1 1 1 1 1 1 1 ...
$ workingday: Factor w/ 2 levels "NotWorkingDay",..: 1 1 1 1 1 1 1 1 1 1 ...
$ weathersit: Factor w/ 4 levels "NoPrecip","Misty",..: 1 1 1 1 1 2 1 1 1 1 ...
$ temp : num [1:17379] 0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ...
$ atemp : num [1:17379] 0.288 0.273 0.273 0.288 0.288 ...
$ hum : num [1:17379] 0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ...
$ windspeed : num [1:17379] 0 0 0 0 0 0.0896 0 0 0 0 ...
$ casual : num [1:17379] 3 8 5 3 0 0 2 1 1 8 ...
$ registered: num [1:17379] 13 32 27 10 1 1 0 2 7 6 ...
$ count : num [1:17379] 16 40 32 13 1 1 2 3 8 14 ...
Now we’re ready build some data visualizations.
Let’s base our visualizations around a series of questions that we want to potentially answer.
How does the total number of rides change by hour of the day?
ggplot (bike,aes (x= hr,y= count)) + geom_boxplot () + theme_bw ()
What about by month?
ggplot (bike,aes (x= mnth,y= count)) + geom_boxplot () + theme_bw ()
By day of week
ggplot (bike,aes (x= weekday,y= count)) + geom_boxplot () + theme_bw ()
Some faceting.
ggplot (bike,aes (x= hr,y= count)) + geom_boxplot () + theme_bw () + facet_wrap (~ workingday,ncol = 1 )
Has the bike share program grown over time? Plot sum of daily rides over time.
%>% group_by (dteday) %>% summarise (sum = sum (count)) %>% ggplot (aes (x= dteday,y= sum)) + geom_line () + theme_bw ()
%>% group_by (dteday) %>% summarise (sum = sum (count)) %>% ggplot (aes (x= dteday,y= sum)) + geom_line () + geom_smooth (method = "lm" , se = FALSE ) + theme_bw ()
`geom_smooth()` using formula = 'y ~ x'
Is there a relationship between temperature and ride count?
ggplot (bike,aes (x= temp,y= count)) + geom_point () + theme_bw ()
Use alpha to alleviate overplotting.
ggplot (bike,aes (x= temp,y= count)) + geom_point (alpha= 0.05 ) + theme_bw ()
Add some color
ggplot (bike,aes (x= temp,y= count,color= workingday)) + geom_point (alpha= 0.1 ) + theme_bw ()
Not sure that this works well, let’s facet.
ggplot (bike,aes (x= temp,y= count,color= workingday)) + geom_point (alpha= 0.1 ) + facet_wrap (~ workingday) + theme_bw ()
Keep playing with this a bit.
ggplot (bike,aes (x= temp,y= count,color= workingday)) + geom_point (alpha= 0.1 ) + facet_wrap (~ weathersit) + theme_bw ()
Manually adjusting colors
ggplot (bike,aes (x= temp,y= count,color= workingday)) + geom_point (alpha= 0.1 ) + facet_wrap (~ workingday) + theme_bw () + scale_color_manual (values= c ("gray" ,"blue" ))
Let’s look at a histogram of temp and see how we can adjust colors of fills.
ggplot (bike,aes (x= temp)) + geom_histogram () + theme_bw ()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Let’s look at just three months out of the year: Jan, Mar, and Jul. Use a filter to isolate those.
%>% filter (mnth == "Jan" | mnth == "Mar" | mnth == "Jul" ) %>% ggplot (aes (x= temp,fill= mnth)) + geom_histogram () + theme_bw ()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Adjust the histograms so that they overlap and have some transparency.
%>% filter (mnth == "Jan" | mnth == "Mar" | mnth == "Jul" ) %>% ggplot (aes (x= temp,fill= mnth)) + geom_histogram (position = "identity" , alpha = 0.4 ) + theme_bw ()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Adjust the colors manually.
%>% filter (mnth == "Jan" | mnth == "Mar" | mnth == "Jul" ) %>% ggplot (aes (x= temp,fill= mnth)) + geom_histogram (position = "identity" , alpha = 0.4 ) + theme_bw () + scale_fill_manual (values= c ("blue" ,"green" ,"red" ))
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
We can also apply palettes. See here for discussion: http://www.sthda.com/english/wiki/ggplot2-colors-how-to-change-colors-automatically-and-manually#use-rcolorbrewer-palettes
%>% filter (mnth == "Jan" | mnth == "Mar" | mnth == "Jul" ) %>% ggplot (aes (x= temp,fill= mnth)) + geom_histogram (position = "identity" , alpha = 0.4 ) + theme_bw () + scale_fill_brewer (palette= "Set2" )
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Finally, let’s add some labels.
%>% filter (mnth == "Jan" | mnth == "Mar" | mnth == "Jul" ) %>% ggplot (aes (x= temp,fill= mnth)) + geom_histogram (position = "identity" , alpha = 0.4 ) + theme_bw () + scale_fill_brewer (palette= "Set2" ) + xlab ("Temperature" ) + ylab ("Count" ) + ggtitle ("Rides by Month by Temperature" ) + labs (fill= "Month" )
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Save the plot to file
ggsave ("plot.jpg" ,width= 6 ,height= 4 )
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
